CN112579782A - Data processing method, knowledge management system, electronic device, and readable storage medium - Google Patents
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Abstract
The embodiment of the application provides a data processing method, a knowledge management system, electronic equipment and a readable storage medium, relates to the technical field of knowledge management, and aims to obtain data to be converted. And extracting knowledge of the data row to be converted based on a knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments. And responding to input operation to acquire original principle data and original skill data. The principle knowledge database is established based on the original principle data, and the skill knowledge database is established based on the original skill data, so that the knowledge management efficiency is improved, and the working efficiency is improved.
Description
Technical Field
The present application relates to the field of knowledge management technologies, and in particular, to a data processing method, a knowledge management system, an electronic device, and a readable storage medium.
Background
With the arousal of data consciousness in recent years, a plurality of enterprises begin to construct data warehouses and data middleboxes, and some enterprises already have better data bases. However, data itself does not generate knowledge, and data is only an impression of the real world in virtual space, and knowledge is higher than data and is more close to the essence of things. After three stages of informatization, big data age and data staging, more and more enterprises become aware of how much innovation is mainly derived from own knowledge assets, and the importance and the value of the innovation exceed those of data assets and even financial assets. The era that knowledge becomes the core asset and core driving force of enterprises begins to come, and more enterprise managers pay attention to the operation and management of knowledge. Enterprises with high knowledge density need to establish an efficient knowledge management system.
At present, many enterprises still indirectly manage knowledge through employee training, document management and data management, human intelligence is needed to refine knowledge and apply knowledge from training courseware, documents and data, and a more efficient knowledge management method is lacked.
Disclosure of Invention
In view of the above, the present application provides a data processing method, a knowledge management system, an electronic device, and a readable storage medium to improve the above problems.
In a first aspect, the present application provides a data processing method applied to a knowledge management system, where a knowledge extractor is prestored in the knowledge management system, and the data processing method includes:
acquiring data to be converted;
extracting knowledge of the data line to be converted based on the knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments;
responding to input operation, and acquiring original principle data and original skill data;
and establishing a principle knowledge database based on the original principle data, and establishing a skill knowledge database based on the original skill data.
In an alternative embodiment, the knowledge extractor comprises a script extractor, and the data to be converted comprises structure class data; the step of extracting the knowledge of the data line to be converted based on the knowledge extractor to obtain a knowledge segment comprises the following steps:
acquiring a predefined field category system, wherein the category system comprises categories, category relations and attributes included by the categories;
extracting at least one instance in the structural class data and the attribute of each instance by utilizing the scripted extractor;
and mapping all the examples and the attributes of each example to the domain category system to obtain a knowledge segment.
In an alternative embodiment, the knowledge extractor comprises a model extractor, and the data to be converted comprises non-structural class data; the step of extracting the knowledge of the data line to be converted based on the knowledge extractor to obtain a knowledge segment comprises the following steps:
acquiring a predefined field category system, wherein the category system comprises categories, category relations and attributes included by the categories;
identifying at least one entity included in the non-structural class data using the model extractor;
classifying all the entities to obtain the category of each entity and the attribute of each entity;
and mapping all the entities and the attributes of each entity to the field type system to obtain a knowledge segment.
In an alternative embodiment, the raw skill data comprises a domain decision map, and the step of obtaining raw skill data comprises:
responding to input operation to obtain at least one service application scene, wherein each service application scene comprises a plurality of subtasks;
responding to decomposition operation, and decomposing each subtask to obtain at least one minimum task, wherein each minimum task represents a workflow configuration file;
and according to the inclusion relation, constructing a domain decision map by using all the service application scenes, all the subtasks and all the minimum tasks, and taking the domain decision map as original skill data.
In an optional embodiment, the knowledge management system stores a format specification configuration file in advance, and the step of establishing a principle knowledge database based on the raw principle data includes:
based on the format specification configuration file, performing data mining on the original principle data to obtain principle knowledge data, wherein the principle knowledge data comprises indexes, rules and models;
and establishing an original knowledge database by using the indexes, the rules and the model.
In an alternative embodiment, the raw skill data comprises a domain decision map, and the step of building a skill knowledge database based on the raw skill data comprises:
obtaining at least one workflow configuration file included in the domain decision map;
principle knowledge data corresponding to each workflow configuration file are obtained from the principle knowledge database, and the principle knowledge data comprise indexes, rules and models;
configuring each index, the rule and the model according to each workflow configuration file to obtain at least one workflow;
and establishing a skill knowledge database based on all the workflows.
In a second aspect, the present application provides a data processing method, which is applied to a knowledge management system, wherein the knowledge management system is prestored with a knowledge extractor, and further comprises a fact knowledge database, a principle knowledge database and a skill knowledge database;
the fact knowledge database acquires data to be converted, extracts knowledge of the data to be converted based on the knowledge extractor to obtain knowledge segments, and establishes the knowledge segments based on the knowledge segments to obtain the knowledge segments;
the principle knowledge database acquires original principle data by responding to input operation and is established based on the original principle data;
the skill knowledge database acquires original skill data by responding to the input operation, and is established and obtained based on the original skill data;
the data processing method comprises the following steps:
performing task processing on tasks included in a preset service scene by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result;
and sending the task processing result to a target system.
In an optional implementation manner, the step of performing task processing on the tasks included in the preset service scenario by using the principle knowledge database, the skill knowledge database, and the fact knowledge database to obtain a task processing result includes:
extracting at least one target principle knowledge data from the principle knowledge database based on tasks included in a preset service scene;
inputting each of the target principle knowledge data into each of the workflows included in the fact knowledge database;
executing each workflow after the target principle knowledge data is input to obtain an execution result;
and taking all the execution results as task processing results.
In a third aspect, the present application provides a knowledge management system comprising:
the data access module is used for acquiring data to be converted;
the information extraction module is used for extracting knowledge of the data line to be converted based on the knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments;
the human-computer interaction module is used for responding to input operation and acquiring original principle data and original skill data;
the knowledge storage module is used for establishing a principle knowledge database based on the original principle data and establishing a skill knowledge database based on the original skill data;
the knowledge reasoning module is used for performing task processing on tasks included in a preset service scene by utilizing the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result;
and the knowledge service module is used for sending the task processing result to a target system.
In a fourth aspect, the present application provides an electronic device, including a processor, a memory and a bus, where the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processor and the memory communicate with each other through the bus, and the processor executes the machine-readable instructions to perform the steps of the data processing method according to any one of the foregoing embodiments; or to perform the steps of the data processing method of any of the preceding embodiments.
In a fifth aspect, the present application provides a readable storage medium, which stores a computer program, and when the computer program is executed, the computer program realizes the steps of the data processing method described in any one of the foregoing embodiments; or implementing the steps of the data processing method of any of the preceding embodiments.
The embodiment of the application provides a data processing method, a knowledge management system, electronic equipment and a readable storage medium, relates to the technical field of knowledge management, and aims to obtain data to be converted. And extracting knowledge of the data row to be converted based on a knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments. And responding to input operation to acquire original principle data and original skill data. The principle knowledge database is established based on the original principle data, and the skill knowledge database is established based on the original skill data, so that the knowledge management efficiency is improved, and the working efficiency is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, several embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a domain category system according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a domain decision map according to an embodiment of the present application.
Fig. 5 is a second flowchart of a data processing method according to an embodiment of the present application.
FIG. 6 is a functional block diagram of a knowledge management system provided in an embodiment of the present application.
Icon: 100-an electronic device; 110-a memory; 120-a processor; 130-knowledge management system; 131-a data access module; 132-an information extraction module; 133-human-computer interaction module; 134-knowledge storage module; 135-knowledge reasoning module; 136-knowledge service module; 140-a communication unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
As introduced by the background, in recent years, with the arousal of data awareness, many enterprises have started to build data warehouses and data middleboards, and some enterprises have already provided a better data base. However, data itself does not generate knowledge, and data is only an impression of the real world in virtual space, and knowledge is higher than data and is more close to the essence of things. After three stages of informatization, big data age and data staging, more and more enterprises become aware of how much innovation is mainly derived from own knowledge assets, and the importance and the value of the innovation exceed those of data assets and even financial assets. The era that knowledge becomes the core asset and core driving force of enterprises begins to come, and more enterprise managers pay attention to the operation and management of knowledge. Enterprises with high knowledge density need to establish an efficient knowledge management system.
At present, many enterprises still indirectly manage knowledge through employee training, document management and data management, human intelligence is needed to refine knowledge and apply knowledge from training courseware, documents and data, and a more efficient knowledge management method is lacked.
In view of this, the data processing method, the knowledge management system, the electronic device and the readable storage medium provided in the embodiments of the present application extract data, classify all the data into a fact knowledge database, a principle knowledge database and a skill knowledge database, and perform task processing together using the databases, thereby efficiently completing knowledge management and knowledge application. The above scheme is explained in detail below.
The above prior art solutions have drawbacks that are the results of practical and careful study, and therefore, the discovery process of the above problems and the solutions proposed by the following embodiments of the present application to the above problems should be the contributions of the applicant to the present application in the course of the present application.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the keys in the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a block diagram of an electronic device 100 according to an embodiment of the present disclosure. The device may include a processor 120, a memory 110, a knowledge management system 130, and a communication unit 140, where the memory 110 stores machine-readable instructions executable by the processor 120, and when the electronic device 100 operates, the processor 120 and the memory 110 communicate with each other through a bus, and the processor 120 executes the machine-readable instructions and performs a data processing method.
The elements of the memory 110, the processor 120 and the communication unit 140 are electrically connected to each other directly or indirectly to realize the transmission or interaction of signals.
For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The knowledge management system 130 includes at least one software functional module that may be stored in the memory 110 in the form of software or firmware. The processor 120 is used to execute executable modules stored in the memory 110, such as software functional modules or computer programs included in the knowledge management system 130.
The Memory 110 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 120 may be an integrated circuit chip having signal processing capabilities. The Processor 120 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and so on.
But may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In the embodiment of the present application, the memory 110 is used for storing a program, and the processor 120 is used for executing the program after receiving the execution instruction. The method defined by the process disclosed in any of the embodiments of the present application can be applied to the processor 120, or implemented by the processor 120.
The communication unit 140 is used to establish a communication connection between the electronic apparatus 100 and another electronic apparatus via a network, and to transmit and receive data via the network.
In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, the Network may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof.
In the embodiment of the present application, the electronic device 100 may be, but is not limited to, a smart phone, a personal computer, a tablet computer, or the like having a processing function.
It will be appreciated that the configuration shown in figure 1 is merely illustrative. Electronic device 100 may also have more or fewer components than shown in FIG. 1, or a different configuration than shown in FIG. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
The steps of the data processing method provided by the embodiment of the present application are explained in detail below based on the structural diagram of the electronic device 100 shown in fig. 1. Referring to fig. 2, fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present disclosure. The data processing method is applied to a knowledge management system, the knowledge management system is prestored with a knowledge extractor, and the data processing method comprises the following steps:
in step S11, data to be converted is acquired.
And step S12, extracting the knowledge of the data row to be converted based on the knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments.
And step S13, responding to the input operation, and acquiring original principle data and original skill data.
And step S14, establishing a principle knowledge database based on the original principle data and establishing a skill knowledge database based on the original skill data.
The extracted knowledge segments are factual data and can include instances, attributes of the instances, relationships between the instances, categories of the instances, and the like. Examples may be persons, animals, plants or other objects, and when an example is a person, the attributes of the example may include height, age, gender, contact, and the like. Relationships between examples may include a couple, a parent, a child and a jiu nephew, etc. When the example is an animal, the attributes of the example may include weight, hair color, species, and the like.
For example, instance one may be Zhang three, whose attributes may include height: 175cm, age: age 30, body weight: 75kg, sex: male, contact mode: 152xxxx1234, and the like. Example two may be plum red, whose attributes may include height: 155cm, age: age 30, body weight: 45kg, sex: female, contact means: 152xxxx 4321. Example one third relates to example two lipsticks as a couple.
The original principle data is principle data and can comprise indexes, rules and models, the indexes can be different in different service scenes, and reference standards are usually provided for the rules. For example, the indicators may be temperature, asset-to-liability ratio, bandwidth pressure values, and the like. Rules may be defined manually, typically as criteria to judge what action to take in conjunction with an index. The model can be used for carrying out feature extraction on the original principle data to obtain an index for reference.
For example, in the context of credit applications in the financial industry. The indicators may include the asset-to-liability ratio and the credit score of the instance, etc. The model may include a credit scoring model based on which the credits for an instance may be scored, resulting in a credit score. The rules may include: the credit application is rejected if the instance's ratio of assets to liabilities is greater than 80%. The method can also comprise the following steps: if the credit score of the instance is below 20 points (full 100 points), the credit application is rejected. The method can also comprise the following steps: if the immediate affiliate of the instance is on the credit blacklist, the credit application is rejected. For example, if a three-way financial institution initiated credit application, whose wife's plum is on the credit blacklist, rejects the three-way initiated credit application.
The skill knowledge is constructed in order to accomplish tasks involved in a certain business scenario. Typically a knowledge of skill comprises a workflow built from indexes, rules and models in the knowledge of principles. At least one workflow forms a minimum task included in a business scenario, at least one minimum task forms a task, and at least one task forms the business scenario.
According to the embodiment of the application, the data is subjected to knowledge extraction, the fact knowledge database is established by using the extracted knowledge segments, the principle knowledge database is established by using the obtained original principle knowledge, and the skill knowledge database is established by using the obtained original skill knowledge, so that the knowledge management efficiency is improved.
As an alternative embodiment, the knowledge extractor includes a script extractor, and the data to be converted includes structure class data. Step S12, extracting knowledge from the data to be converted based on the knowledge extractor, and obtaining knowledge segments can be implemented by the following steps:
and acquiring a predefined field category system, wherein the category system comprises categories, category relations and attributes included by the categories.
And extracting at least one instance and the attribute of each instance in the structural class data by using a script extractor.
And mapping all the examples and the attributes of each example to a domain category system to obtain a knowledge segment.
Referring to fig. 3, fig. 3 is a schematic view of a domain type system according to an embodiment of the present disclosure. The domain type system can be constructed in advance. As shown in FIG. 3, the domain type hierarchy can include people, events, places, items, organizations, intangible (intangible property), and the like. Each of the above categories may also include further attributes, for example, for an organization category, which may include company, local merchant, educational institution, government institution, group, creator, organization member, address, date of establishment, and the like. The attributes can be further classified. For example, the community may include a performance community, a sports team, and the like.
The script extractor can be written based on script Query languages such as sql (structured Query language) and hieveql. The method mainly aims at the knowledge extraction of the structured and semi-structured database.
Thus, the embodiment of the application queries the relevant examples and the attributes of the examples from the data to be converted by using the script extractor. And mapping the examples and the attributes into a predefined domain category system, thereby obtaining knowledge segments and facilitating the subsequent establishment of a factual knowledge database based on the knowledge segments.
In another alternative embodiment, the knowledge extractor comprises a model extractor, and the data to be converted comprises non-structural class data. Step S12, extracting knowledge of the data row to be converted based on the knowledge extractor, and obtaining knowledge segments can also be implemented by the following steps:
and acquiring a predefined field category system, wherein the category system comprises categories, category relations and attributes included by the categories.
At least one entity included in the unstructured class data is identified using a model extractor.
And classifying all the entities to obtain the category of each entity and the attribute of each entity.
And mapping all the entities and the attribute of each entity to a field type system to obtain a knowledge fragment.
The model extractor may be a model obtained by performing machine learning by using a training data set in advance. The data to be converted can be non-structural data such as images, videos, texts, voices and the like.
And carrying out data extraction on the non-structural data through a pattern extractor so as to identify entities and attributes of the entities in the data to be converted. For example, the data to be converted, which is of the type of an image, may be subjected to image recognition using an image recognition model, thereby recognizing at least one entity included in the image, and attributes of the entity.
It should be noted that the entity is similar to the above example, and as with the example, may be a person, animal, plant or other object, and when the example is a person, the attributes of the example may include height, age, sex, contact information, and the like. Relationships between examples may include a couple, a parent, a child and a jiu nephew, etc. When the example is an animal, the attributes of the example may include weight, hair color, species, and the like.
For example, entity one may be Zhang III, and its attributes may include height: 175cm, age: age 30, body weight: 75kg, sex: male, contact mode: 152xxxx1234, and the like. Entity two may be plum red, and its attributes may include height: 155cm, age: age 30, body weight: 45kg, sex: female, contact means: 152xxxx 4321. The relationship between entity one, three and entity two, plum red is a couple.
Therefore, in the embodiment of the application, knowledge extraction can be performed on various types of data through different types of knowledge extractors, various types of data can be converted into knowledge, and the knowledge conversion efficiency is improved.
In an alternative embodiment, where the raw skill data includes a domain decision map, step S3, obtaining the raw skill data may be accomplished by:
and responding to the input operation to obtain at least one service application scene, wherein each service application scene comprises a plurality of subtasks.
And responding to the decomposition operation, and decomposing each subtask to obtain at least one minimum task, wherein each minimum task represents a workflow configuration file.
And constructing a domain decision map by using all service application scenes, all subtasks and all minimum tasks according to the inclusion relation, and taking the domain decision map as original skill data.
For example, as a possible implementation scenario, a schematic diagram of a domain decision map for an online marketing domain may be shown in fig. 4.
The online marketing domain may include a plurality of business application scenarios, such as advertisement placement, e-commerce marketing, channel sales, member marketing, social marketing, private domain marketing, and the like. The service application scenes of advertisement delivery can be divided into three categories: before the activity starts, during the activity and after the activity ends.
Each business scenario may be divided into at least one subtask, for example, the activity may be decomposed into subtasks such as platform portfolio recommendation and media testing before starting. The activity can be decomposed into subtasks of diagnosis, pre-inspection, early warning and the like during delivery. After the activity is finished, the subtasks such as activity summary, diagnosis after delivery and the like can be decomposed.
Each subtask can be decomposed into at least one minimum task, for example, subtask-platform combination recommendation can be decomposed into minimum tasks such as conventional customer-platform combination recommendation and customized customer delivery platform combination recommendation. Subtask-media testing can be broken down into the minimal tasks of conventional new media docking generic testing, new media pass-back DeviceID (device ID) docking testing, and media-associated back-end testing. Subtask-in-delivery diagnostics can be broken down into the smallest tasks of page click diagnostics, page diagnostics, site diagnostics, media frequency diagnostics, media delivery time diagnostics, customized crowd diagnostics, and general crowd diagnostics. The subtask-preview can be decomposed into conventional active data preview, abnormal traffic preview and back-end traffic preview. The subtask-early warning can be decomposed into the minimum tasks of routine pre-inspection during the activity, media return DeviceID early warning during the activity, CTR (Click-Through-Rate) early warning during the OTV (Online TV) activity, CTR early warning during the Display activity, completion Rate early warning during the activity, Reach (permission) completion Rate early warning, advertisement putting environment early warning and the like.
The subtask-activity summary can be decomposed into minimum tasks such as advertisement delivery environment summary, abnormal flow summary, loads completion rate summary, KPI completion rate summary by Reach, and conventional activity completion rate summary. The subtask-post-delivery diagnosis can be decomposed into minimum tasks such as media (advertisement form) coverage diagnosis, back-end lead acquisition channel diagnosis, back-end target population accurate customer delivery strength diagnosis, conventional customer delivery strength diagnosis, industry media exposure click diagnosis and the like.
Therefore, for various service scenes in different fields, the field decision map can be obtained according to the method, and the technical knowledge database can be conveniently generated subsequently. Meanwhile, a global visual angle is provided for internal employees of the enterprise, and the knowledge can be shared and spread conveniently.
In an alternative embodiment, step S14, the knowledge management system pre-stores the format specification configuration file, and the creating of the principle knowledge database based on the raw principle data can be implemented by the following steps:
and based on the format specification configuration file, performing data mining on the original principle data to obtain principle knowledge data, wherein the principle knowledge data comprises indexes, rules and models.
And establishing an original knowledge database by using the indexes, the rules and the models.
The format specification configuration file may be any one of a Model specification PMML (Predictive Model Markup Language), an ONNX (Open Neural Network Exchange), and a specification DRL (Drools rule description Language) of rules.
Alternatively, the principle knowledge database may also be configured to receive key elements input by a user (e.g., a knowledge engineer) based on a template, and obtain principle knowledge data including indexes, rules, and models based on the key elements, so as to build the original knowledge database using the indexes, rules, and models.
In an alternative embodiment, where the raw skill data comprises a domain decision map, step S4, building the skill knowledge database based on the raw skill data may be accomplished by:
at least one workflow configuration file included in the domain decision map is obtained.
And acquiring principle knowledge data corresponding to each workflow configuration file from a principle knowledge database, wherein the principle knowledge data comprise indexes, rules and models.
And configuring each index, rule and model according to each workflow configuration file to obtain at least one workflow.
A skill knowledge database is built based on the entire workflow.
The workflow configuration file is the minimum task shown in fig. 4. For example, in a credit application scenario in the financial industry, a workflow obtained based on a workflow configuration file may be based on first searching whether an applicant or an immediate relative of the applicant is in a credit blacklist from a fact database, performing an anti-fraud review if neither the applicant nor the immediate relative of the applicant is in the credit blacklist, performing an amount adjustment process on an application limit of the applicant in combination with a personal credit status of the applicant after the review is passed, and finally ending the workflow.
Therefore, based on the skill knowledge database, workflows in different service application scenes can be called, and the corresponding workflows can be conveniently called from the skill knowledge database directly according to the service scenes in actual application so as to complete tasks of corresponding service scenes. The knowledge operation efficiency is improved, and the working efficiency is also improved.
The application provides a data processing method which is applied to a knowledge management system, wherein the knowledge management system is prestored with a knowledge extractor and further comprises a fact knowledge database, a principle knowledge database and a skill knowledge database.
The fact knowledge database is obtained by acquiring data to be converted, extracting knowledge of the data to be converted based on a knowledge extractor to obtain knowledge segments and establishing the knowledge segments based on the knowledge segments.
The principle knowledge database is obtained by responding to input operation, acquiring original principle data and establishing the original principle data. The skill knowledge database acquires original skill data by responding to input operation, and is established based on the original skill data.
Referring to fig. 5, fig. 5 is a second schematic flowchart of a data processing method according to an embodiment of the present disclosure. The method comprises the following steps:
and step S21, performing task processing on the tasks included in the preset service scene by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result.
In step S22, the task processing result is sent to the target system.
In an optional implementation manner, the task processing is performed on the tasks included in the preset service scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database, and the step of obtaining the task processing result includes:
and extracting at least one target principle knowledge data from the principle knowledge database based on tasks included in the preset service scene.
The target principle knowledge data is entered into workflows comprised by the fact knowledge database.
Executing each workflow after the target principle knowledge data is input to obtain an execution result;
and taking all execution results as task processing results.
It is understood that the principle of the steps of the above method is similar to that of the steps of the data processing method shown in fig. 2, and the description thereof is omitted here.
Based on the same inventive concept, please refer to fig. 6 in combination, and fig. 6 is a functional block diagram of a knowledge management system according to an embodiment of the present application. The embodiment of the present application further provides a knowledge management system corresponding to the data processing method shown in fig. 2 and 5, and the system includes:
and the data access module 131 is configured to acquire data to be converted.
And the information extraction module 132 is configured to extract knowledge of the data row to be converted based on the knowledge extractor to obtain knowledge segments, and establish a fact knowledge database based on the knowledge segments.
And the human-computer interaction module 133 is configured to respond to an input operation and acquire original principle data and original skill data.
And the knowledge storage module 134 is used for establishing a principle knowledge database based on the original principle data and establishing a skill knowledge database based on the original skill data.
And the knowledge reasoning module 135 is configured to perform task processing on the tasks included in the preset service scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result.
And the knowledge service module 136 is used for sending the task processing result to the target system.
Since the principle of solving the problem in the embodiment of the present application is similar to the data processing method shown in fig. 2 and fig. 5 in the embodiment of the present application, the implementation principle of the system may refer to the implementation principle of the method, and repeated details are not repeated.
The embodiment of the present application also provides a readable storage medium, in which a computer program is stored, and when the computer program is executed, the steps of the data processing method described above are implemented.
In summary, the embodiments of the present application provide a data processing method, a knowledge management system, an electronic device, and a readable storage medium, by acquiring data to be converted. And extracting knowledge of the data row to be converted based on a knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments. And responding to input operation to acquire original principle data and original skill data. The principle knowledge database is established based on the original principle data, and the skill knowledge database is established based on the original skill data, so that the knowledge management efficiency is improved, and the working efficiency is improved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. A data processing method applied to a knowledge management system, the knowledge management system having a knowledge extractor prestored therein, the data processing method comprising:
acquiring data to be converted;
extracting knowledge of the data line to be converted based on the knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments;
responding to input operation, and acquiring original principle data and original skill data;
and establishing a principle knowledge database based on the original principle data, and establishing a skill knowledge database based on the original skill data.
2. The data processing method of claim 1, wherein the knowledge extractor comprises a scripted extractor, and the data to be converted comprises structure class data; the step of extracting the knowledge of the data line to be converted based on the knowledge extractor to obtain a knowledge segment comprises the following steps:
acquiring a predefined field category system, wherein the category system comprises categories, category relations and attributes included by the categories;
extracting at least one instance in the structural class data and the attribute of each instance by utilizing the scripted extractor;
and mapping all the examples and the attributes of each example to the domain category system to obtain a knowledge segment.
3. The data processing method according to claim 1 or 2, wherein the knowledge extractor comprises a model extractor, and the data to be converted comprises non-structural class data; the step of extracting the knowledge of the data line to be converted based on the knowledge extractor to obtain a knowledge segment comprises the following steps:
acquiring a predefined field category system, wherein the category system comprises categories, category relations and attributes included by the categories;
identifying at least one entity included in the non-structural class data using the model extractor;
classifying all the entities to obtain the category of each entity and the attribute of each entity;
and mapping all the entities and the attributes of each entity to the field type system to obtain a knowledge segment.
4. The data processing method of claim 1, wherein the raw skill data comprises a domain decision map, and the step of obtaining raw skill data comprises:
responding to input operation to obtain at least one service application scene, wherein each service application scene comprises a plurality of subtasks;
responding to decomposition operation, and decomposing each subtask to obtain at least one minimum task, wherein each minimum task represents a workflow configuration file;
and according to the inclusion relation, constructing a domain decision map by using all the service application scenes, all the subtasks and all the minimum tasks, and taking the domain decision map as original skill data.
5. The data processing method of claim 1, wherein the knowledge management system is pre-stored with a format specification configuration file, and the step of building a principles knowledge database based on the raw principles data comprises:
based on the format specification configuration file, performing data mining on the original principle data to obtain principle knowledge data, wherein the principle knowledge data comprises indexes, rules and models;
and establishing an original knowledge database by using the indexes, the rules and the model.
6. The data processing method of claim 1, wherein the raw skill data comprises a domain decision map, and the step of building a skill knowledge database based on the raw skill data comprises:
obtaining at least one workflow configuration file included in the domain decision map;
principle knowledge data corresponding to each workflow configuration file are obtained from the principle knowledge database, and the principle knowledge data comprise indexes, rules and models;
configuring each index, the rule and the model according to each workflow configuration file to obtain at least one workflow;
and establishing a skill knowledge database based on all the workflows.
7. The data processing method is characterized by being applied to a knowledge management system, wherein the knowledge management system is prestored with a knowledge extractor and also comprises a fact knowledge database, a principle knowledge database and a skill knowledge database;
the fact knowledge database acquires data to be converted, extracts knowledge of the data to be converted based on the knowledge extractor to obtain knowledge segments, and establishes the knowledge segments based on the knowledge segments to obtain the knowledge segments;
the principle knowledge database acquires original principle data by responding to input operation and is established based on the original principle data;
the skill knowledge database acquires original skill data by responding to the input operation, and is established and obtained based on the original skill data;
the data processing method comprises the following steps:
performing task processing on tasks included in a preset service scene by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result;
and sending the task processing result to a target system.
8. The data processing method according to claim 7, wherein the step of performing task processing on the tasks included in the preset business scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain the task processing result comprises:
extracting at least one target principle knowledge data from the principle knowledge database based on tasks included in a preset service scene;
inputting each of the target principle knowledge data into each of the workflows included in the fact knowledge database;
executing each workflow after the target principle knowledge data is input to obtain an execution result;
and taking all the execution results as task processing results.
9. A knowledge management system, comprising:
the data access module is used for acquiring data to be converted;
the information extraction module is used for extracting knowledge of the data line to be converted based on the knowledge extractor to obtain knowledge segments, and establishing a fact knowledge database based on the knowledge segments;
the human-computer interaction module is used for responding to input operation and acquiring original principle data and original skill data;
the knowledge storage module is used for establishing a principle knowledge database based on the original principle data and establishing a skill knowledge database based on the original skill data;
the knowledge reasoning module is used for performing task processing on tasks included in a preset service scene by utilizing the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result;
and the knowledge service module is used for sending the task processing result to a target system.
10. An electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the data processing method according to any one of claims 1 to 6; or to perform the steps of the data processing method of any of claims 7 to 8.
11. A readable storage medium, characterized in that the readable storage medium stores a computer program which, when executed, implements the steps of the data processing method of any one of claims 1 to 6; or implementing the steps of a data processing method according to any of claims 7 to 8.
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